function [C, sigma] = dataset3Params(X, y, Xval, yval)
%EX6PARAMS returns your choice of C and sigma for Part 3 of the exercise
%where you select the optimal (C, sigma) learning parameters to use for SVM
%with RBF kernel
%   [C, sigma] = EX6PARAMS(X, y, Xval, yval) returns your choice of C and 
%   sigma. You should complete this function to return the optimal C and 
%   sigma based on a cross-validation set.
%

% You need to return the following variables correctly.
C = 1;
sigma = 0.1;

% ====================== YOUR CODE HERE ======================
% Instructions: Fill in this function to return the optimal C and sigma
%               learning parameters found using the cross validation set.
%               You can use svmPredict to predict the labels on the cross
%               validation set. For example, 
%                   predictions = svmPredict(model, Xval);
%               will return the predictions on the cross validation set.
%
%  Note: You can compute the prediction error using 
%        mean(double(predictions ~= yval))
%

C0= [ 0.01 0.03 0.1 0.3 1 3 10 30]';
sigma0= [0.01 0.03 0.1 0.3 1 3 10 30]';
% for(i=1:8)
% C=C0(i);
% for(j=1:8)
% sigma=sigma0(j);
% model= svmTrain(X, y, C, @(x1, x2) gaussianKernel(x1, x2, sigma));
% predictions = svmPredict(model, Xval);
% err(i,j)=mean(double(predictions ~= yval));
% end
% end
% [a,b]=min(err);
% [c,d]=min(a);
% C=d;
% sigma=b(d);


% =========================================================================

end
